A Statistical Process Control Approach for Automatic Anti-Islanding Detection Using Synchrophasors ∗ School
Yuanjun Guo∗ , Kang Li∗ , D. M. Laverty∗ of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, BT9 5AH, UK Email:
[email protected] [email protected];
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Abstract—Anti-islanding protection is becoming increasingly important due to the rapid installation of distributed generation from renewable resources like wind, tidal and wave, solar PV, bio-fuels, as well as from other resources like diesel. Unintentional islanding presents a potential risk for damaging utility plants and equipment connected from the demand side, as well as to public and personnel in utility plants. This paper investigates automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through a wide area measurement system, which is based on Phasor Measurement Unit (PMU) technology. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real system and the results have confirmed that the proposed method can correctly identify the fault and islanding site. Index Terms—Distributed Generation, Anti-Islanding, Phasor Measurement, Statistical Process Control, Principal Component Analysis.
I. I NTRODUCTION Distributed generation is comprised of small-scale and geographically distributed energy sources. Energy sources include renewables, like wind, tidal and wave, solar PV, bio-fuels, etc, and also traditional ones like diesel generators [1]. In recent years, the twin challenges of sustainable and secure energy supply to meet ever increasing energy demand and environment concerns in regards to climate change and pollution have spurred the rapid installation of distributed generation worldwide. Today, some networks frequently operate with greater than 10% of their power supplied by distributed generation, and Ireland has at times operated with over 50% of generation supplied by wind energy [2] [3]. High penetration of distributed generations has placed considerable impact on the power system planning, scheduling, operation, control, protection, and maintenance. Of particular interest here is the ’islanding’ situation where a
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distributed generator is supplying power to a location without power from the utility being present [4]. Islanding can occur intentionally, but also unintentionally. For the latter case, this is often caused by an unexpected interruption to the utility supply in a region where an embedded generator is operating in parallel to the utility grid. Islanding may pose a risk of damaging utility plant and customer connected equipment, and it also presents a danger to utility personnel working to restore the utility supply and to the public as the utility is no longer in control of power quality and earthing. For these reasons, islanded operation while connected to the utility network is generally forbidden [5]. Methods of anti-islanding detection have become the most challenging and important aspect in designing the electrical power system with distributed generations [4]. An important technique in anti-islanding detection is to reliably detect the power island condition, and the two most common incumbent methods are Rate-of-Changeof-Frequency (ROCOF) and Vector Shift which rely on a power imbalance to detect islanding. These techniques can fail when the power imbalance may not be large enough to activate the protection to trip the inter-tie break to disconnect the power island from the utility, or an event may occur across the grid which is misjudged by ROCOF or Vector Shift relay as an islanding event. This leads to the nuisance tripping which can further cause ’cascade tripping’, causing significant economic and social loss. Therefore, to improve the reliability of islanding detection in this area has become an important research topic. Wide-area Synchrophasor technology provides the anti-islanding detectors at distributed generators with real-time information, delivered using secure Internet Protocol technologies, so that nuisance trips may be avoided, sensitivity improved and cascade tripping can be prevented. Laverty et al [2] introduced synchrophasor for anti-islanding detection, and a prototype of the detector has been tested and validated. In their method, the
thresholds the detector should operate were dependent on analysis of the network, and a system of phase monitoring stations has been installed to acquire this data. This paper follows the same technical route, with the focus on automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through the aforementioned wide area measurement system. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of a fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real power system and the results have confirmed the efficacy of the proposed approach. This paper is organized as follows. Section II introduces the Synchrophasor anti-islanding technique. Section III describes the data acquired and the PCA technique. Section IV presents the data analysis results based on PCA and fault reconstruction. Section V concludes the paper. II. S YNCHROPHASOR A NTI - ISLANDING The method of anti-islanding detection proposed by Laverty et al [2] aims to use Synchrophasor technology in an intuitive way, intended to make the system appealing to generator owners and thus ensure its adoption as a protection scheme. Fig.1 explains the method as well as the location map of PMU monitoring stations for establishing typical phase variation in British/Irish utility networks. The map also shows the wide area measurement system which has been developed in conjunction with Scottish & Southern Energy.
frequency and phase angle at a specific and accurate time. The standard for representing Synchrophasors is IEEE C37.118.2-2011 [6]. The reference signal is transmitted to the generator site by means of Internet Protocol (IP) telecommunications. This may be via the Internet (public network) or via a secure utility network. At the generator site, the reference signal is compared to the Synchrophasor acquired at the generator terminals. The time signatures of the Synchrophasors are aligned so that phasors taken at the same instant are compared. The phase difference between the generator and the reference site are compared against known typical operating conditions. When the phase between the generator and the reference site becomes abnormally large, the generator is considered to be islanded and will be disconnected from the utility. The network of PMUs include four PMUs in southern England, one in Manchester, one in Tealing, five on the Orkney islands, and two in Shetland. Additional PMUs operate on the Irish network providing similar data. Data is transmitted to Queen’s University Belfast in an ASCII plain text format and stored on a server. This allows for post analysis of events, determination of suitable antiislanding relay event thresholds and online simulation of anti-islanding relays. III. DATA ACQUIRED A ND I NTRODUCTION TO PCA A. PMU Phasor Measurements The pure sinusoidal waveform y(t) = Am cos(ωt+φ) is commonly represented as a phasor Y = Yr + Yi = √ (Am / 2)(ejφ ), where φ depends on the definition of the time scale. According to IEEE Standard C37.118.1-2011 [7], this basic concept is adapted as the representation of AC power system sinusoidal signals. PMU phasor measurements are required to be synchronized to UTC time with sufficient accuracy. Data collected from real system PMUs of several sites successfully captured islanding events. This paper aims to apply the statistical process control method to assist with automatic anti-islanding detection.
Fig. 2. Fig. 1.
Enlarged View of Frequency Dip
Proposed Synchrophasor Anti-Islanding detection scheme
A reference signal is acquired from a dependable utility site using PMU technology, meaning that the signal will contain at minimum the voltage amplitude,
Due to the stability of power gird, the three-phase phasors stay in a narrow range for most of the time. However, some events such as transmission outages may happen from time to time, as a result, frequency dip will
occur across the entire power grid. Some weak sites will be islanded due to the dramatic change of frequency. In order to detect the frequency change as well as the islanding site based on the PMU phasor measurements, two data sets were arranged. One data set (PMU-1, PMU-2, PMU-3) contains a frequency dip caused by a 1 GW loss in bulk generation as shown in Fig.2. The second data set contains a similar fault as well as an islanding event occurred in response to the frequency dip (PMU-1, PMU-4, PMU-5).
equation represents the distance in principal component subspace. However, monitoring the output variable via T 2 based on the first k PCs is not sufficient. A change in correlation among the variables increases the projection on the residual subspace. Such new events can be detected by computing the squared prediction errors (SPE) of the residuals of new observations [10].
B. PCA based Fault detection
This statistic is referred to as the Q-statistic, or distance to the model. When the process is normal, this value should be small and under the upper confidence control limit.
This paper presents a scheme for automatic antiislanding fault detection and fault reconstruction based on PCA projections. This approach makes use of a normal PCA model to decompose the data and get the scores and loadings. Suppose the normalized data matrix has m samples and (rows) and N variables (columns), denoted as X ∈ Rm×N . PCA decomposes the data matrix into a score matrix T ∈ RN ×k and a loading matrix P ∈ Rm×k (k ≤ m is the number of retained principal components) [8]. The Principal Components (PCs) are represented by the loading and score vectors so that PCA can decompose the observation matrix X as: X = t1 pT1 + t2 pT2 + ... + tk pTk + E = TPT + E
(1)
ti are score vectors of the data, which contain information on how the samples are correlated to each other. pi are the loadings, also are the eigenvectors of the covariance matrix, E is the residual matrix. After the PCA model is built, a sample matrix can be decomposed into two parts, +X ˜ X=X
(2)
= PPT X ∈ Sp is the projection on the Where X ˜ = (I − PPT )X ∈ principal component subspace. And X Sr is the projection on the residual subspace. These represent the modelled and unmodelled variations of X respectively. and X ˜ are orthogonal to each other, the Note that X dimension of Sp is the number of principal components k, and the dimension of Sr is m − k. The important statistic for fault detection is given by Hotelling’s T 2 , which is the sum of normalized squared scores defined as [9], Ti2 = ti λ−1 tTi = xi pλ−1 pT xTi
2 ˜ = (I − PPT )X2 SP E ≡ X
C. Fault Reconstruction Although fault detection is the first step in process monitoring, fault identification and reconstruction are in a sense more important in order to apply corrective actions on the faulty variable [11]. In the presence of a process fault Fi , the sample vector x can be represented using a fault direction vector ξi , which characterizes the effect of Fi on the actual measurements, The task of fault reconstruction is to best estimate x, using the PCA model and fault direction ξ. The reconstructed vector xi is obtained by correcting x in the direction ξi . xi = x − fi ξi
(5)
fi is an estimate of the fault magnitude f which measures the displacement in the direction ξi . This fault vector can represent a sensor fault as well as a process fault. Let ξiT = [1, 0, ..., 0] so that it can represent a failure of the first sensor or the first input process variable in the sample vector x. The magnitude f can indicate abrupt change occurs in the process. The distance between xi and Sp is given by the magnitude 2 of the residual vector x˜i , or the SPE of xi . 2 2 2 x − fi ξ˜i = ˜ x − f˜i ξ˜i0 (6) SP Ei = x˜i = ˜ Where f˜i ≡ fi ξ˜i , and ξ˜i0 = ξ˜i / ξ˜i , represents the normalized residual direction for the fault vector ξi . Minimizing SP Ei leads to:
(3)
where ti is the ith row of k score vectors from PCA model, and λ−1 is a diagonal matrix containing the inverse eigenvalues associated with the k eigenvectors. The
(4)
or
T dSP Ei = 2ξ˜i0 (˜ x − f˜i ξ˜i0 ) = 0 ˜ d fi
(7)
T T ˜ = ξ˜i0 (I − ppT )x f˜i = ξ˜i0 x
(8)
Therefore, the fault vector can be reconstructed as:
T ξ˜0 (I − ppT )x f˜i = i fi = ˜ ˜ ξi ξi
(9)
Eq.(9) now can be used to monitor the fault developing over time for each input variable. Disturbance or fault happens in any variable can be detected and identified by the fault reconstruction vector.
that the highlighted area around 3am is the frequency dip inception point as it violates both the control limits with very high statistical value. In order to make comparisons between the two data sets, PCA monitoring also has been applied on Data Set 2 which includes a site that islanded post frequency dip. Results are shown in Fig.4. T−square and Q−statistic Chart of Data Set 2
IV. M AIN R ESULTS PCA
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Two data sets were processed by PCA to detect the fault sections in all the sites, and then fault reconstruction based on the loadings from PCA model result were used to separate islanding site out of the synchronized sites.
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First PCA models were produced using Data Set 1 and Data Set 2 separately. According to the dynamics of the real power system, data collected for a single day can not represent the entire power system performance over time. Therefore, for each dataset, PCA should be applied separately to verify the variance. In PCA, the PCs that can retain most of the variance information are kept. In this paper, the most obvious variations come from the frequency dip and the followed islanding event. Other variations in system demand, wind generation and other factors are considered as the normal dynamic conditions. The consideration here is to flag out the possible frequency dip for further analysis, and leave the normal variations both in the principal subspace and residual subspace. Results of PCA modelling against time of a day are shown in Fig.3.
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T-square and Q Statistic Chart of Data Set 2
The situation now is more complicated and dynamic than that of Data Set 1 due to the islanding occurring on one of the sites. From Fig.4,two highlighted area from both the T 2 and Q statistic result are with very high values which should be paid enough attention for further analysis. Islanding event was possibly happened during 3am to 8am as well as 6pm to 8pm. B. Fault Reconstruction Result To further identify the islanding site after the PCA modelling, fault reconstruction method has been implemented on the frequency component of these two data sets. The magnitude of reconstructed fault is used to identify the islanded site. The fault reconstruction result of Data Set 1 is shown in Fig.5. 0.15
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Fig.5 indicates that the reconstructed faults of PMU-1, PMU-2 and PMU-3 are straight lines with magnitudes around zero, except one obvious abrupt change at the fault inception position. It is believed that these three sites are generally synchronized both before and after the frequency dip across the power grid. For comparison, fault reconstruction of Data Set 2 has also been done with the result shown in Fig.6. 4
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The faults reconstructed in Fig.6 are different from Fig.5. These lines are also straight but the two sections of fluctuations on each of them represent the response behaviour after the frequency dip occurs. The green line of PMU-4 is different from PMU-1 and PMU-5 and with a relative high magnitude. High value of reconstructed fault indicates that this variable contributes more significantly in the arranged data. It is also shown that PMU-4 was unsynchronized with the entire grid after the two big frequency changes, and PMU-4 signal kept generating power on its own frequency for a while before resynchronization. It is worth noting that due to the PCA decomposition, which is based on the covariance of the data, the loadings may not be identical for different group of data. This explains why the data from PMU-1 in the two data sets can get the different values of fault reconstruction. V. C ONCLUSION This paper has shown that PCA based statistic process control approach is a powerful tool for automatic antiislanding detection. This method is capable of analysing the covariance among variables by projecting data into principal subspace and residual space. Fault occurrence detection can be achieved by T 2 and Q statistic charts. If abnormal situations are identified by the PCA model, the fault reconstruction technique can then be deployed to identify the exact faulty process variable. The application to real system data has shown that the method can detect the faults and identify the source accurately.
The work however has also raised some important issues with respect to the implementation of the linear PCA method. The performance of PMU under dynamic conditions should be taken into consideration. The monitoring statistics based on linear PCA needs to be extended to dynamic and nonlinear cases to meet the requirements of accuracy. R EFERENCES [1] N. Jenkins, R. Allan, P. Crossley, D. Kirschen, and G. Strbac. Embedded Generation, Power and Energy Series 31. London, UK: Inst. Elect. Eng, 2000. [2] DM Laverty, DJ Morrow, R. Best, and M. Cregan. Anti-islanding detection using synchrophasors and internet protocol telecommunications. In Innovative Smart Grid Technologies (ISGT Europe), 2011 2nd IEEE PES International Conference and Exhibition on, pages 1–5. IEEE, 2011. [3] Eirgrid. System demand data. www.eirgrid.com, 2011. [4] P. Crossley, F. Ilar, and D. Karlsson. System protection schemes in power networks: existing installations and ideas for future development. In Developments in Power System Protection, 2001, Seventh International Conference on (IEE), pages 450–453. IET, 2001. [5] Energy Networks Association et al. Distribution Code: Modification to the Distribution Code to Implement a Change to Engineering Recommendation G59/2 Relating to the Limits of Direct Current Injection. London, U.K., 2011. [6] ’IEEE Standard for Synchrophasor Data Transfer for Power Systems’: IEEE Std C37.118.2-2011. http://standards.ieee.org/findstds/standard/C37.118.22011.html [Accessed: April 12 2011], 2011. [7] ’IEEE Standard for Synchrophasor Measurements for Power Systems’: IEEE Std C37.118.1-2011. http://standards.ieee.org/findstds/standard/C37.118.12011.html [Accessed: April 12 2012], 2011. [8] I.T. Jolliffe and MyiLibrary. Principal component analysis, volume 2. Wiley Online Library, 2002. [9] I.L. Dryden and K.V. Mardia. Statistical shape analysis, volume 4. John Wiley & Sons New York, 1998. [10] J.V. Kresta, J.F. Macgregor, and T.E. Marlin. Multivariate statistical monitoring of process operating performance. The Canadian Journal of Chemical Engineering, 69(1):35–47, 1991. [11] R. Dunia and S. Joe Qin. Subspace approach to multidimensional fault identification and reconstruction. AIChE Journal, 44(8):1813–1831, 2004.
